Abstract: This paper proposes two methods based on sparse representation to deal with face recognition with one training image per person. In the proposed two methods, it generates the multiple images by shifting the original image or reconstructing the original image using Principle Component Analysis(PCA) method, and regards new images as training samples, and then applies Sparse Representation-based Classification(SRC) on new training samples set. Experiments on the well-known ORL database show that the proposed two methods are about 5.56% and 1.67%, more accurate than original SRC method when considering in the context of single sample face recognition problem. In addition, extensive experimentation reported in the paper suggests that the proposed two methods achieve lower error recognition rate than Shifted images +PCA, Shifted images +LDA, PCA reconstructed images +LDA, PCA, LDA.

Key words: face recognition, single sample, Principle Component Analysis(PCA), Sparse Representation-based Classification(SRC)

摘要： 提出2种基于稀疏表征SRC的单样本人脸识别方法。通过Shift或PCA重构的方法产生冗余样本，将生成的新样本作为训练样本，运用SRC进行识别分类。在ORL人脸库上的实验证明，在单样本情况下，2种方法分别比原SRC方法提高了5.56%和1.67%。与Shifted images +PCA、Shifted images +LDA、PCA重构人脸图像+LDA、PCA、LDA等方法做比较，实验表明，2个方法均具有良好的识别性能。

关键词: 人脸识别, 单样本, 主成分分析, 基于稀疏表征的分类

CLC Number: 

• TP391.41